1. Field
The present invention relates to pattern recognition, feature extraction, statistical learning, and object detection technology such as human detection technology. More particularly, the present invention relates to an image forming system that can extract a discriminative feature for the color appearance of an object, or can form a hidden discriminative color distribution pattern contrasting with other objects of the same or different categories, and to an apparatus and method of extracting a discriminative color feature.
2. Description of the Related Art
A representative color feature of an object's appearance is widely used to detect and track an object in a video frame. Generally, a color histogram is used to represent the color feature. In order to capture the spatial layout of the color distribution, it may be appropriate to use a histogram of an independent sub-area that is comprised in an object area. In addition to the above scheme, other schemes such as motion information, shape information, constrained geometry, etc., may be used to compensate a color model to obtain improved detection results.
Various attempts have been made to solve the problems of tracking coherent moving objects using the color model. Examples of the attempts include a probabilistic tracking scheme using a color appearance, a color histogram modeling and probability map detection scheme, a scheme of comparing a plurality of similar searching windows based on the color model, a scheme of extracting a skin tone based on a statistical model, etc.
As described above, the representative color feature for the object appearance is widely used for object detection in the video frame. When representing the color feature, the color histogram is generally used to determine a location and a size of a moving object and calculate a color histogram of the moving object. In a subsequent video frame, the similarity map of the histogram model is calculated. Next, a blob analysis technology can cluster pixel areas with high similarity to a histogram model to blobs, which indicate a location of the moving object with high probability.
Many schemes focus on how to effectively represent the color feature of the moving object. Specifically, the most important issue is how to discriminate the moving object from a non-moving object, that is, other moving objects of the same or different category and a background, instead of forming the original color distribution of the object completely. In the case of the moving object, the moving object consists of many parts. For example, when it is assumed that the moving object is a human being, the human being includes a face/head, a dressed upper body and a dressed lower body. The color appearance of the human being may be similar to other image areas, particularly in an environment with a complex background. In this environment, it is necessary to select a discriminative color gamut from the human body as color features, instead of selecting the representative color feature of the whole human body.